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下一代测序技术的出现和检测费用不断下降清除了稀有变异检测的技术障碍,可以检测出包括常见变异和稀有变异在内的数以千万的遗传变异,其中绝大部分的变异都是稀有变异。这种情况对统计分析方法和结果解释提出了新的挑战。当前最为流行的全基因组分析方法主要针对常见变异间上位效应的检测问题,家畜育种中较少涉及稀有变异间、稀有变异-常见变异上位效应的研究。本综述探讨使用贝叶斯多元回归方法将上位效应检测单位由成对SNP扩展到基因组窗口间的检测,整合基因组信息进一步缩减数据集维度,并使用基因组窗口后验关联概率控制假阳性比例。这种新的研究策略无疑具有以下两个优良特性:1)这种方法将基因组窗口中所有SNP作为一个整体,可以利用该区域内的所有信息检测上位效应;2)该方法可以大幅度减少多重检测数量。其次,中国畜牧企业表型数据丰富,缺乏基因组测序数据,本研究借鉴单步基因组预测原理,设计检测包括上位效应在内的"穷"GWAS方法。
Abstract：The emergence of next generati on sequencing technology and the declining detection cost have removed the technical barriers to rare mutation detection.Tens of millions of genetic variations,including common and rare variations,can be detected,and most of them are rare variations..Such situation brings a new challenge to statistical analysis methods and interpretation of results.At present,the most popular method for whole genome analysis is mainly aimed at the detection of epistatic effects between common variants.Researches on the epistatic effects between rare variants or between rare and common variants are seldom involved in livestock breeding.In this review,the basic unit of interaction analysis was extended from a pair of SNPs to the genomic windows by using Bayesian multivariate linear regression(BMR).Genomic information was integrated to further reduce the dataset dimension,and the false positive ratio(FPR) was controlled by using the genome window posterior probability of association(WPPA).This new paradigm of epistasis analysis might have the following two excellent features:(1) this method could take all SNP in the genome window as a whole and use all the information in the region to detect the epistatic effect;(2) This method could largely reduce the number of multiple detections.Moreover,there are abundant phenotypic data and lack of genome sequencing data in China's livestock enterprises.Therefore,the"poor"GWAS method,including the detection of epistatic effects would be designed based on the theory of single-step genomic BLUP(ss GBLUP).
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基本信息:
DOI：10.13417/j.gab.037.003304
中图分类号:S813.1
引用信息:
[1]梅步俊,王志华.家畜全基因组分析中稀有变异上位效应检测方法[J].基因组学与应用生物学,2018,37(08):3304-3312.DOI:10.13417/j.gab.037.003304.
基金信息:
国家自然科学基金(31460594;31760660);; 河套学院教学研究项目(HTXYJZ14005)共同资助
2017-10-14
2017-10-14
2017-10-14